Data Science

Date Science is one of the Emerging innovations which making ready for future. It is a mashup of numerous desciplines which make up this Latest innovation interesting to get the hang of utilizing R and Python Programming dialects. Its primary segments are measurements and machine realizing which are utilized to apply in a few plans of action to enhance their decision of basic leadership and to comprehend the more about their Business.

About The Course

Quality Thought is one of the Top best organizations which is putting forth Data Science preparing in Hyderabad. Keeping the elements of this cutting edge innovation are measurements, R and Python, Machine Learning on the choice to give right mix of this innovation to our understudies in more useful Approach and simple to learn and comprehend technique. The course enables you to take in Data Science Technology from the scratch and furthermore the point of this preparation is to make our Trainees the best learned and useful situated. Before the finish of the preparation they ought to have the capacity to convey the machine inclining Models easily. Quality Thought is one of the main Training organizations which offers Data Science preparing in Hyderabad.


Data Science Course Content (R, Python, ML)

Getting started with Data Science and Recommender Systems

Introduction to Data Science, importance of Data Science, statistical and analytical methods, deploying Data Science for Business Intelligence, transforming data, machine learning and introduction to Recommender systems.

Reasons to Use Data Science – Project Life cycle

How Data Science solves real world problems, Data Science Project Life Cycle, principles of Data Science, introduction to various BI and Analytical tools, data collection, introduction to statistical packages, data visualization tools, R Programming, predictive modelling, machine learning, artificial intelligence and statistical analysis.

Data Conversion

Converting data into useful information, Collecting the data, Understand the data, Finding useful information in the data, Interpreting the data, Visualizing the data

Terms of Statistics

Descriptive statistics, Let us understand some terms in statistics, Variable


Dot Plots, Histogram, Stemplots, Box and whisker plots, Outlier detection from box plots and Box and whisker plots

Set & rules of probability, Bayes Theorem

What is probability?, Set & rules of probability, Bayes Theorem


Probability Distributions, Few Examples, Student T- Distribution, Sampling Distribution, Student t- Distribution, Poison distribution


Stratified Sampling, Proportionate Sampling, Systematic Sampling, P – Value, Stratified Sampling

Tables & Analysis

Cross Tables, Bivariate Analysis, Multi variate Analysis, Dependence and Independence tests ( Chi-Square ), Analysis of Variance, Correlation between Nominal variables

Acquiring Data

Boxplot in R programming, understanding distribution and percentile, identifying outliers, Rstudio Tool, various types of distribution like Normal, Uniform and Skewed.

R Programming Course Content:-

Introduction to R

R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.


R Functions, code compilation and data in well-defined format called R-Packages, learn about R-Package structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.

Sorting Dataframe

R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.

Matrices and Vectors

Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.

Reading data from external files

Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.

Generating plots

Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.

Analysis of Variance (ANOVA)

Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.

K-means Clustering

K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.

Association Rule Mining

Introduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.

Regression in R

Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.

Analyzing Relationship with Regression

Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit

Advance Regression

Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.

Logistic Regression

Logistic Regression Mean, Logistic Regression in R.

Advance Logistic Regression

Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.

Receiver Operating Characteristic (ROC)

Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.

Kolmogorov Smirnov Chart

Data analysis with R, understanding the WALD test, MC Fadden’s pseudo R-squared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.

Database connectivity with R

Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.

Integrating R with Hadoop

Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.

Python Programming Course Content :-

Learn the Basics

Data Science Tutorials

Advanced Tutorials

Machine Learning in Data Science:-

Deploying machine learning for data analysis, solving business problems, using algorithms for searching patterns in data, relationship between variables, multivariate analysis, interpreting correlation, negative correlation.

Deep dive into Data Transformation

Data Transformation key phases Data Mapping and Code Generation, Data Processing operation, data patterns, data sampling, sampling distribution, normal and continuous variable, data extrapolation, regression, linear regression model.

Data Testing and Assessment

Data analysis, hypothesis testing, simple linear regression, Chi-square for assessing compatibility between theoretical and observed data, implementing data testing on data warehouse, validating data, checking for accuracy, data operational monitoring capabilities.

Data Model, Algorithms & Prediction

Various techniques of data modelling and generating algorithms, methods of business prediction, prediction approaches, data sampling, disproportionate sampling, data modelling rules, data iteration, and deploying data for mission-critical applications.

Data Segmentation and Analysis

Working with large data sets in data warehouses, data clustering, grouping, horizontal & vertical slicing, data sharding in partitioning, clustering algorithms, K-means Clustering for analysis and data mining, exclusive clustering, hierarchy clustering, Mahout Clustering algorithm and Probabilistic Clustering, nearest neighbour search, pattern recognition, and statistical classification.

GPS INFOTECH (Software Solutions)
Contact person: prakash
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